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Rationale for searching sequence
databases
June 22, 2005
Writing Topics due today
Writing projects due July 8
Learning objectives

Review of Smith-Waterman Program
FASTA and BLAST programs. Psi-Blast
Workshop-Use of Psi-BLAST to determine
sequence similarities. Use BLASTx to gain
information on gene structure.
FASTA (Pearson and Lipman
1988)
This is a combination of word search and SmithWaterman algorithm
The query sequence is divided into small words of
certain size.
The initial comparison of the query sequence to
the database is performed using these “words”.
If these “words” are located on the same diagonal
in an array the region surrounding the diagonals
are analyzed further.
Search time is only proportional to size of
database (not database*query sequence)
The FASTA program is the uses Hash tables.
These tables speed the process of word search.
Query Sequence
= TCTCTC
123456 (position number)
Database Sequence = TTCTCTC
1234567 (position number)
You choose to use word size = 4 for your
table (total number of words in your table is
44 = 256)
?
Sequence (total
of 256)
TCTC
CTCT
TTCT
Position w/in query
1,3
2
Position w/in DB
2,4
3
1
Offset (Q minus DB)
-1 or -3 or 1
-1
FASTA Steps
1
Different offset values
2
Identical offset
values in a
contiguous sequence
Diagonals are extended
Local regions of
identity are found
Rescore the local regions
using PAM or Blos. matrix
4
3
Eliminate short diagonals
below a cutoff score
Create a gapped alignment in
a narrow segment and then
perform S-W alignment
Summary of FASTA steps
1. Analyzes database for identical matches that are contiguous
(between 5 and 10 amino acids in length (same offset values)).
2. Longest diagonals are scored again using the PAM matrix (or
other matrix). The best scores are saved as “init1” scores.
3. Short diagonals are removed.
4. Long diagonals that are neighbors are joined. The score for this
joined region is “initn”.
5. A S-W dynamic programming alignment is performed around the
joined sequences to give an “opt” score.
Thus, the time-consuming S-W step is performed only on top
initn scoring sequences
The ktup value
•The ktup (for k-tuples) value stands for the length of the word
used to search for identity.
•For proteins, a ktup value of 3 would give a hash table of 203
elements (8000 entries).
•The higher the ktup value the less likely you will get a match
unless it is identical (remember the dot plots).
•The lower the ktup value the more background you will have
•The higher the ktup value the faster analysis (fewer
diagonals).
The following rules typically apply when using FASTA:
ktup
analysis____________________
1
proteins- distantly related
2
proteins- somewhat related (default)
3
DNA-default
FASTA Versions
FASTA-nucleotide or protein sequence searching
FASTx/-compares a translated DNA query sequence
FASTy to a protein sequence database (forward
or backward translation of the query)
tFASTx/-compares protein query sequence to
tFASTy DNA sequence database that has been
translated into three forward and three
reverse reading frames
FASTA Statistical Significance
A way of measuring the significance of a score considers the mean
of the random score distribution.
The difference between the similarity score for your single alignment
and the mean of the random score distribution is normalized by
the standard deviation of that random score
distribution. This is the Z-score.
Higher Z-scores are better because
the further the real score is from this mean (in standard deviation units)
the more significant it is.
FASTA Statistical Significance
Z score for a single alignment=
(similarity score - mean score from database)
standard deviation from database
Stand. Dev. =

2
(
scores)
 scores2 Total#ofSequences
Total#ofSequences
Mean similarity scores
of complete database
Mean similarity scores
of related records
FASTA statistics (cont.)
Using the distribution of the z-scores in the database, the FastA
program can estimate the number of sequences that would
be expected to produce, purely by chance, a z-score greater than or
equal to the z-score obtained in the search.
The E value
(false positive expectation value)
The Expect value (E) is a parameter that describes the number
of “hits” one can "expect" to see just by chance when
searching a database of a particular size. It decreases
exponentially as the Similarity Score (S) increases (inverse
relationship). The higher the Similarity Score, the lower
the E value. Essentially, the E value describes the random
background noise that exists for matches between two
sequences. The E value is used as a convenient way to
create a significance threshold for reporting results. When
the E value is increased from the default value of 10 prior
to a sequence search, a larger list with more low-similarity
scoring hits can be reported. An E value of 1 assigned to a
hit can be interpreted as meaning that in a database of the
current size you might expect to see 1 match with a similar
score simply by chance.
E value
E = K•m•n•e-λS
Where K is constant, m is the length of the query
sequence, n is the length of the database sequence,
λ is the decay constant, S is the similarity score.
If S increases, E decreases exponentially.
If the decay constant increases, E decreases
exponentially
If m•n increases, the “search space” increases and
there is a greater chance for a random “hit”, E
increases. Larger database will increase E.
When z the E()
Evaluating the Results of FASTA
Best
SCORES
Init1: 2847 Initn: 2847 Opt: 2847
z-score: 2609.2 E(): 1.4e-138
Smith-Waterman score: 2847; 100.0% identity in 413 overlap
Good
SCORES
Init1: 719 Initn: 748 Opt: 793
z-score: 734.0 E(): 3.8e-34
Smith-Waterman score: 796; 41.3% identity in 378 overlap
Mediocre
SCORES
Init1: 249 Initn: 304 Opt: 260
z-score: 243.2 E(): 8.3e-07
Smith-Waterman score: 270; 35.0% identity in 183 overlap
BLAST
Basic Local Alignment Search Tool
Speed is achieved by:
Pre-indexing the database before the search
 Parallel processing

Uses a hash table that contains
neighborhood words rather than just random
words.
Neighborhood words
The program declares a hit if the word taken from
the query sequence has a score >= T when a
scoring matrix is used.
This allows the word size (W (this is similar to
ktup value)) to be kept high (for speed) without
sacrificing sensitivity.
If T is increased by the user the number of
background hits is reduced and the program will
run faster
Comparison Matrices
In general, the BLOSUM series is thought to be superior to the
PAM series because it is derived from areas of conserved sequences.
It is important to vary the parameters when performing a sequence
comparison. Similarity scores for truly related sequences are
usually not sensitive to changes in scoring matrix and gap penalty.
Thus, if your “hits list” holds up after changing these parameters
you can be more sure that you are detecting similar sequences.
Which Program should one use?
Most researchers use methods for
determining local similarities:
Smith-Waterman (gold standard)
Do not find every possible alignment
 FASTA
of query with database sequence. These
 BLAST
are used because they run faster than S-W

}
What are the different BLAST
programs?
blastp
 compares an amino acid query sequence against a protein sequence
database
blastn
 compares a nucleotide query sequence against a nucleotide
sequence database
blastx
 compares a nucleotide query sequence translated in all reading
frames against a protein sequence database
tblastn
 compares a protein query sequence against a nucleotide sequence
database dynamically translated in all reading frames
tblastx
 compares the six-frame translations of a nucleotide query sequence
against the six-frame translations of a nucleotide sequence
database. Please note that tblastx program cannot be used with the
nr database on the BLAST Web page.
When to use the correct program
Problem
Program
Explanation
Identify
Unknown
Protein
BLASTP;
FASTA3
General protein
comparison. Use ktup=2
for speed; ktup=1 for
sensitive search.
Smith-Waterman
Slower than FASTA3
and BLAST but provides
maximum sensitivity
TFASTX3;TFASTY3;
TBLASTN
Use if homolog cannot
be found in protein
databases; Approx. 33%
slower
Psi-BLAST
Finds distantly related
sequences. It replaces
the query sequence with
a position-specific score
matrix after an initial
BLASTP search. Then it
uses the matrix to find
distantly related
sequences
When to use the correct program (cont. 1)
Problem
Program
Identify
new
orthologs
TFASTX3;TFASTY3
TBLASTN:TBLASTX
Identify
EST
Sequence
FASTX3;FASTY3;
BLASTX;TBLASTX
Identify
DNA
Sequence
FASTA;BLASTN
Explanation
Use PAM matrix <=20 or
BLOSUM90 to avoid detecting
distant relationships. Search
EST sequences w/in the same
species.
Always attempt to translate
your sequence into protein
prior to searching.
Nucleotide sequence
comparision
Choosing the database
Remember that the E value increases
approximately linearly with database size.
When searching for distant relationships always
use the smallest database likely to contain the
homolog of interest.
Thought problem: If the E-value one obtains for a
search is 12 in Swiss-PROT and the E-value one
obtains for same search is 74 in PIR how large is
PIR compared to Swiss-PROT?
74/12 = ~6
Filtering Repetitive Sequences
Over 50% of genomic DNA is repetitive
This is due to:





retrotransposons
ALU region
microsatellites
centromeric sequences, telomeric sequences
5’ Untranslated Region of ESTs
Example of ESTs with simple low complexity regions:
T27311
GGGTGCAGGAATTCGGCACGAGTCTCTCTCTCTCTCTCTCTCTCTCTC
TCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTCTC
Filtering Repetitive Sequences
(cont. 1)
Programs like BLAST have the option of
filtering out low complex regions.
Repetitive sequences increase the chance of
a match during a database search
PSI-BLAST
PSI-position specific iterative
a position specific scoring matrix (PSSM) is
constructed automatically from multiple HSPs of
initial BLAST search. Normal E value is used
This PSSM is the new scoring matrix for a second
BLAST search. Low E value is used E=.001.
Result-1) obtain distantly related sequences
2) find out the important residues that
provide function or structure.